Artificial Intelligence Applications in Chemistry - American Chemical

24

Elucidation

Downloaded by UNIV OF CALIFORNIA SANTA BARBARA on February 27, 2018 | https://pubs.acs.org Publication Date: April 30, 1986 | doi: 10.1021/bk-1986-0306.ch024

by

of

Structural Fragments

C o m p u t e r - A s s i s t e d I n t e r p r e t a t i o n of IR S p e c t r a 1

Hugh B. Woodruff, Sterling A.Tomellini ,and Graham M. Smith Merck Sharp & Dohme Research Laboratories, Rahway, NJ 07065 Since its introduction to the scientific community in late 1980, PAIRS (Program for the Analysis of IR Spectra) has been used successfully by a large number of researchers. Recent improvements to PAIRS have made this package incorporate most of the aspects of expert systems. The improvement highlighted in this paper is the capability for scientists to inquire of the system why a particular interpretation result was achieved. This capability enhances the ability for scientists to learn from the knowledge base of interpretation rules present in PAIRS. It also simplifies the process by which the PAIRS knowledge base can be refined through incorporation of improved rules from expert spectroscopists. One of the more i n t e r e s t i n g areas available f o r development i n analyt i c a l spectroscopy i s the generation of algorithms and software capable of i n t e r p r e t i n g IR spectra. A number of papers have been published recently on computerized i n t e r p r e t a t i o n of v i b r a t i o n a l spectra (1-22). Thegeneration of such software requires the a n a l y t i c a l chemist to understand the i n t e r p r e t a t i o n process and be able to translate the process into an algorithm which the computer can perform. While generating the actual computer code i s by no means t r i v i a l , the chemical knowledge required to solve the i n t e r p r e t a t i o n problem makes a chemist and not a computer s c i e n t i s t the l i k e l y producer of such a program. Among the most widely d i s t r i b u t e d of these i n t e r p r e t a t i o n programs i s a package c a l l e d PAIRS Program f o r the Analysis of IR Spectra) which has been d i s t r i b u t e d by the authors and the Quantum Chemistry Program Exchange to nearly 100 researchers. The program i s available i n both IBM mainframe and DEC VAX versions. A s i m p l i f i e d schematic of the information flow i n PAIRS i s shown i n Figure 1. Spectral information i n the form of a d i g i t i z e d IR spectrum including peak l o c a t i o n , width and i n t e n s i t y values may be entered either 1

Current address: University of New Hampshire, Durham, NH 03824 0097-6156/ 86/0306-0312$06.00/0 © 1986 American Chemical Society

Pierce and Hohne; Artificial Intelligence Applications in Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 1986.


24.

WOODRUFF ET AL.

Computer-Assisted Interpretation of IR Spectra

313

i n t e r a c t i v e l y or from a f i l e created previously, perhaps with the aid of a d i g i t i z i n g tablet. Since the introduction of PAIRS i n 1980, considerable e f f o r t has been expended on improving various aspects of the package to make i t more valuable to researchers. A version of PAIRS capable of running on a N i c o l e t FTIR instrument-based minicomputer was developed to eliminate the time required to d i g i t i z e spectra and to make the program a v a i l a b l e to the p r a c t i c i n g a n a l y t i c a l spectroscop i s t (12). Recently, versions of PAIRS capable of running on other FTIR systems have been reported (23, 24). Generating Interpretation Rules The generation of i n t e r p r e t a t i o n rules f o r PAIRS has proven to be a time-consuming and often inexact process. Many man-years were required to generate the f i r s t set of rules. Trulson and Munk (18) emphasized the massive e f f o r t required f o r rule development i n t h e i r report on t h e i r promising work on a table-driven approach to infrared spectral i n t e r p r e t a t i o n . Rule development and subsequent testing are generally much more time consuming than e i t h e r acquiring test spectra or programming the i n t e r p r e t a t i o n routines. One of the strengths of PAIRS i s the a c c e s s i b i l i t y of the i n terpretation r u l e s i n a form that i s e a s i l y understandable and modif i a b l e by the s c i e n t i s t . To accomplish t h i s feat, a s p e c i a l E n g l i s h - l i k e language known as CONCISE (Computer Oriented Notation Concerning Infrared Spectral Evaluation) was developed (19). CONCISE has a very small (62 words) and well-defined vocabulary which can be mastered by non-computer-oriented s c i e n t i s t s . I t cons i s t s of if-then-else l o g i c and begin-done blocking. Once the vocabulary and structure of CONCISE are known, the s c i e n t i s t i s free to create or change i n t e r p r e t a t i o n rules at w i l l . In order to expand the usefulness of the PAIRS package, an automated rule generation program has been developed. An advantage of automated rule generation i s that a more mathematical and uniform method of determining expectation values can be developed and used. (An expectation value i s a measure of the l i k e l i h o o d of occurrence for the presence of a p a r t i c u l a r f u n c t i o n a l i t y i n the unknown compound.) A detailed d e s c r i p t i o n of the algorithms used f o r the automated rule generator i s presented elsewhere (21). The s i m p l i c i t y and c l a r i t y of CONCISE has been retained i n the automated rule generator which creates CONCISE i n t e r p r e t a t i o n rules for PAIRS based on a representative set of IR spectra. The rule generator uses peak p o s i t i o n , i n t e n s i t y , and width tables produced by an automated peak p i c k i n g routine. This method reduces the dependency on published frequency c o r r e l a t i o n data and enhances the usefulness of data already a v a i l a b l e . A l l work was done using the version of PAIRS running on a N i c o l e t 1180 minicomputer and programs generated have been optimized f o r t h i s system. CONCISE rules are generated based on the frequency of occurrence of peaks i n compounds i n a spectral database. Good interpret a t i o n rules have been created using a r e l a t i v e l y small number of spectra i n the database. To recreate i n t e r p r e t a t i o n rules f o r the 168 classes of compounds currently addressed by PAIRS i n an automated manner would require a s u b s t a n t i a l e f f o r t and a better


314

ARTIFICIAL INTELLIGENCE APPLICATIONS IN CHEMISTRY

spectral database than currently e x i s t s . However, the automated rule generator provides the tools to accomplish t h i s task and to expand the current rule base.


Tracing Interpretation Rules The discussion thus f a r has centered on input to the i n t e r p r e t e r ; however, the s c i e n t i s t i s perhans most interested i n the informat i o n returned by PAIRS. The r e s u l t s were previously l i m i t e d to a numerical i n d i c a t i o n of the l i k e l i h o o d that any p a r t i c u l a r functiona l i t y or sub-functionality i s present. While the rules upon which interpretations are based are available i n an E n g l i s h - l i k e language, CONCISE, i t i s normally a rather d i f f i c u l t process to determine why a given f u n c t i o n a l i t y was assigned a given value. The usefulness of PAIRS would be greatly enhanced, e s p e c i a l l y as a research t o o l , i f the program were able to provide the user with a clear trace of the decision making process. Very recent e f f o r t s have resulted i n an improved version of PAIRS which not only allows the user to question which f u n c t i o n a l i t i e s may be present, but also why they are thought to be present (22). Major changes and additions were required to make PAIRS capable of providing an e a s i l y understandable trace of the i n t e r p r e t a t i o n . The interpreter required the vast majority of these modifications, including the addition of a number of new subroutines. F u l l use was made of the decompiling features already present i n the i n t e r p r e t e r . Therefore, input-output and decision c o n t r o l l i n g routines make up the majority of the subroutines added. The decision c o n t r o l l i n g routines a c t u a l l y serve a dual purpose. Not only do these routines decide which data should be printed during a trace, but they also keep track of the progress of the interpreter as i t makes i t s way through the i n t e r p r e t a t i o n r u l e s . Thus, the c o n t r o l l i n g routines know at any given moment which rules have already been interpreted and which rules remain to be interpreted. The rule compiler was modified to create a f i l e containing the "header" names, which are the names of the major f u n c t i o n a l i t i e s . The CONCISE i n t e r p r e t a t i o n rules were not changed during t h i s process. Now the user i s pressented with three options f o r i n t e r p r e t i n g a spectrum: 1.) trace the decision making process f o r a l l f u n c t i o n a l i t i e s ; 2.) trace the decision making process f o r any of the major f u n c t i o n a l i t i e s (e.g., acid) and i t s corresponding s u b - f u n c t i o n a l i t i e s (e.g., acid-saturated); or 3.) interpret the spectrum without any tracing as was done previous to the modifications described i n t h i s paper. In any case, an entire i n t e r p r e t a t i o n takes place and, therefore, a numerical i n d i c a t i o n i s available for the l i k e l i h o o d that each functional i t y and sub-functionality i s present. I t i s important to remember that the if-then-else l o g i c of the CONCISE language forces the interpreter to follow one unique path through the i n t e r p r e t a t i o n r u l e s , a path dictated by the spectral data entered. A very important consequence of being able to follow only one path i s that a trace of the decision making process can give information about what decisions were made but cannot give any information about what decisions might have been made had the spect r a l data been d i f f e r e n t . Knowing what decisions were made can, however, give a good i n d i c a t i o n why a given f u n c t i o n a l i t y might have been reported at a lower value than expected.



24.

WOODRUFF E T A L .


315

The best way to demonstrate the added c a p a b i l i t y and increased v e r s a t i l i t y of the i n t e r p r e t e r due to the t r a c i n g feature i s through example. Since the i n t e r p r e t e r generally bases a good deal of importance on peak i n t e n s i t y information, i t i s obvious that mixtures and larger molecular weight compounds w i l l often cause the i n t e r p r e t e r to return l e s s than desirable r e s u l t s . In cases where the i n t e n s i t i e s are lower than would normally be expected for a given f u n c t i o n a l i t y , a valuable feature of the modified program i s the a b i l i t y to see quickly what decisions have been made and why these decisions were made. The a n t i b i o t i c actinospectacin, the structure of which i s given below, was chosen to demonstrate the improved i n t e r p r e t e r . A spectrum of actinospectacin (published i n Volume 10 of the B u l l e t i n of the International Center of Information on A n t i b i o t i c s ) was d i g i t i z e d with the r e s u l t i n g peak data being presented i n Table I. The peak data i n Table I were entered into the i n t e r p r e t e r without any empiric a l formula information. The sample state entered r e f l e c t e d the fact that the spectrum was taken as a KBr p e l l e t . Table I I contains the twenty f u n c t i o n a l i t i e s and s u b - f u n c t i o n a l i t i e s which the i n t e r p r e t e r predicted as most l i k e l y to be present i n the sample. (The *1*, *2*, and *3* terminology indicated one, two, or three occurrences, respect i v e l y , of alpha branching or unsaturation i n the alcohol.) Prev i o u s l y t h i s information was e s s e n t i a l l y a l l that the user could learn using PAIRS without investing the time necessary to decipher the CONCISE rules for the f u n c t i o n a l i t i e s i n question. The improved version of PAIRS, however, allows the user to ask, f o r example, "Why was "sulfone" indicated with such a high expectation value?". I f the data i n Table 1 are reinterpreted with the decision process for the f u n c t i o n a l i t y "sulfone" being traced, the user learns that the high l i k e l i h o o d for a "sulfone" i s due to the presence of the 1330 and 1351 cm""l bands of i n t e n s i t y 7 and 6, r e s p e c t i v e l y , the 1121 and 1145 cm"-'- bands of i n t e n s i t y 7 and 9, r e s p e c t i v e l y , and the presence of more than two bands between 1090 and 1170 cm with i n t e n s i t i e s greater than 7. The actual decision trace i s given i n Figure 2. Should the user suspect that these bands are due to another functiona l i t y , knowledge of how these bands were used i n p r e d i c t i n g the presence of a "sulfone" may allow the i n t e r p r e t e r ' s p r e d i c t i o n of a high l i k e l i h o o d of "sulfone" to be l e s s highly regarded. Conversely, one may suspect the presence of a p a r t i c u l a r funct i o n a l i t y but discover that the i n t e r p r e t e r predicts that functional i t y with a low expectation value. Knowing the structure of actinospectacin, one would expect that "ketone" should be predicted to be present with a f a i r l y high expectation value. The i n t e r p r e t e r , however, returns a value of 0.01 for the l i k e l i h o o d of presence of the "ketone". In t h i s case, the user learns that the low expectation value f o r "ketone" was based on the absence of any peak with i n t e n s i ty 7 or greater i n the carbonyl region between 1571 and 1800 cm~^-. In the case of an unknown compound, knowledge of the i n t e r p r e t e r ' s decisions can give the user added i n s i g h t s and ideas, e s p e c i a l l y when the spectrum i s not i d e a l for a given f u n c t i o n a l i t y . The user, i n any case, now has the a b i l i t y to work with the program to see i f minor v a r i a t i o n s i n the data would r e s u l t i n d i f f e r e n t and possibly more reasonable i n t e r p r e t a t i o n s . -1




Digitized Spectrum PAIRS (Interpreter)

Chemical Functionality Predictions.

CONCISE Rules

Figure 1. Information flow i n PAIRS.

S t r u c t u r e o f the a n t i b i o t i c

actinospectacin.


24.

WOODRUFF ET AL.


317


Table I. D i g i t i z e d Actinospectacin Spectrum Peak No.

P o s i t i o n (cm

Relative Intensity

Width

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

3527 3401 3311 3254 3071 2962 2796 2486 1645 1629 1581 1566 1460 1429 1392 1351 1330 1271 1235 1215 1190 1176 1145 1121 1107 1087 1078 1046 1037 1024 999 981 952 936 923 891 875 859 814 728

9 10 10 10 9 9 6 2 5 5 4 4 8 6 8 6 7 2 3 3 6 7 9 7 8 9 10 9 9 9 7 3 4 4 7 2 3 5 3 5

Broad Broad Broad Broad Broad Average Broad Average Average Average Average Average Average Average Average Sharp Average Average Average Average Average Sharp Average Average Average Average Average Average Average Sharp Sharp Average Sharp Sharp Average Average Average Average Average Average



318

Table I I .

PAIRS I n t e r p r e t a t i o n R e s u l t s


FUNCTIONALITY 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

for Actinospectacin

EXPECTATION VALUE

ALCOHOL SULFONE OLEFIN-(NON-AROMÏ 0LEFIN-CHR=CH2 ALCOHOL-PHENOL ALC0H0L-PRIM(*1*) ALCOHOL-PRIMARY ALCOHOL-SEC-(*1*) ALCOHOL-SEC-(*2*) ALCOHOL-SEC-RING ALCOHOL-SECONDARY ALCOHOL-TERT-(*1*) ALCOHOL-TERT-(*2*) ALCOHOL-TERT-(*3*) ALCOHOL-TERT-RING ALCOHOL-TERTIARY SULFONAMIDE SULFONAMIDE-PRIM SULFONAMIDE-SEC SULFONAMIDE-TERT

0.99 0.85 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75

F U N C T I O N A L I T Y SULFONE PASSED I N I T I A L E M P I R I C A L FORMULA TEST PEAK QUERY ANY P E A K ( S ) POSITION: 1290 - 1360 INTENSITY: 7 - 10 WIDTH : SHARP TO AVERAGE ANSWER YES PEAK QUERY ANY P E A K ( S ) POSITION: 1110 - 1170 INTENSITY: 7 - 10 WIDTH: SHARP TO BROAD ANSWER YES ACTION SET SULFONE TO 0 . 5 0 0 CURRENT V A L U E = 0 . 5 0 0 PEAK QUERY AT L E A S T 2 P E A K ( S ) POSITION: 1260 - 1360 INTENSITY: 4 - 1 0 WIDTH: SHARP TO AVERAGE ANSWER YES ACTION ADO 0 . 1 0 0 TO SULFONE CURRENT V A L U E = 0 . 6 0 0 PEAK QUERY AT L E A S T 2 P E A K ( S ) POSITION: 1260 - 1360 INTENSITY: 7 - 10 WIDTH: SHARP TO AVERAGE ANSWER NO PEAK QUERY AT L E A S T 2 P E A K ( S ) POSITION: 1 0 6 5 - 1170 INTENSITY: 4 - 1 0 WIDTH: SHARP TO AVERAGE ANSWER YES ACTION ADD 0 . 1 0 0 TO SULFONE CURRENT V A L U E = 0 . 7 0 0 PEAK QUERY AT L E A S T 2 P E A K ( S ) POSITION: 1065 - 1170 INTENSITY: 7 - 1 0 WIDTH: SHARP TO AVERAGE ANSWER YES----ACTION ADD 0 . 1 5 0 TO SULFONE CURRENT V A L U E = 0 . 8 5 0

Figure 2. Trace of sulfone f u n c t i o n a l i t y during i n t e r p r e t a t i o n of actinospectacin.


WOODRUFF E T A L .

24.


319

This point i s i l l u s t r a t e d by a second example. A vapor-phase spectrum of p r o p i o n i t r i l e was obtained and i t s d i g i t i z a t i o n i s shown i n Table I I I . For the sake of example, assume the s c i e n t i s t entered the 2246 cuT* peak as average rather than sharp. The i n t e r p r e t a t i o n would r e s u l t i n l i k e l i h o o d s of 0.90 f o r isocyanate and 0.30 f o r n i t r i l e . Performing the i n t e r p r e t a t i o n with the tracing function turned on would quickly show that the rules base the d i s t i n c t i o n between isocyanate and p r o p i o n i t r i l e very heavily on the width of the peak i n the v i c i n i t y of 2260 cm *. Reinterpreting t h i s spectrum with the correct, sharp width entered f o r the 2246 cm"* peak r e s u l t s i n a n i t r i l e l i k e l i h o o d of 0.50 and isocyanate of 0.40.


-

Table I I I . Peak No. 1 2 3 4 5 6 7 8 9 10 11

D i g i t i z e d P r o p i o n i t r i l e Spectrum

P o s i t i o n (cm 1) 2246 2996 1461 2950 1431 1074 787 2892 1319 1386 546

Relative Intensity

Width

10 8 7 6 5 4 3 3 2 1 1

Average Average Average Average Average Average Average Average Average Average Average

Summary Through the addition of automated spectrum input on instrument-based computers, automated rule generation, and automatic tracing of d e c i sion r u l e s , PAIRS has been enhanced to be an even more valuable t o o l for the spectroscopist. PAIRS i s available f o r d i s t r i b u t i o n from the Quantum Chemistry Program Exchange, Indiana U n i v e r s i t y , Bloomington, IN 47405 (Catalog No. QCPE 497). Literature Cited 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Gray, N.A.B. Anal. Chem. 1975, 47, 2426. Woodruff, H.B.; Munk, M.E. J . Org. Chem. 1977, 42, 1761. Woodruff, H.B.; Munk, M.E. Anal. Chim. Acta 1977, 95, 13. Zupan, J . Anal. Chim. Acta 1978, 103, 273. Visser, T.: Van der Maas, J.H. J . Raman Spectros. 1978, 7, 125. Visser, T.; Van der Maas, J.H. J . Raman Spectros. 1978, 7, 278. Leupold, W-R; Domingo, C.; Niggemann, W.; Schrader, B. Fresenius' Z. Anal. Chem. 1980, 303, 337. Woodruff, H.B.; Smith, G.M. Anal. Chem. 1980, 52, 2321. Visser, T.; Van der Maas, J.H. Anal. Chim. Acta 1980, 122, 337. Varmuza, K. "Pattern Recognition i n Chemistry"; Springer-Verlag; New York, 1980, No. 2, Lecture Notes i n Chemistry Series Woodruff, H.B.; Smith, G.M. Anal. Chim. Acta 1981, 133, 545. Tomellini, S.A.; Saperstein, D.D.; Stevenson, J.M.; Smith G.M.; Woodruff, H.B.; Seelig, P.F. Anal. Chem. 1981, 53, 2367.



320


13. Farkas, M.; Markos, J . ; Szepesvary, P.; Bartha, I.; Szalontai, G.; Simon, Z. Anal. Chim. Acta/Computer Techniques and Optimization 1981, 133, 19. 14. Szalontai, G.; Simon, Z.; Csapo, Z.; Farkas, M.; P f e i f e r , Gy. Anal. Chim. Acta/Computer Techniques and Optimization 1981, 133 303. 15. Debska, B.; Duliban, J . ; Guzowska-Swider, B.; Hippe, Z. Anal. Chim. Acta/Computer Techniques and optimization 1981, 133, 303. 16. Frank, I.E.; Kowalski, B.R. Anal. Chem. 1982, 54, 232R. 17. Zupan, J . Anal. Chim. Acta 1982, 139, 143. 18. Trulson, M.O.; Munk, M.E. Anal. Chem. 1983, 55, 2137. 19. Smith, G.M.; Woodruff, H.B. J . Chem. Inf. Comp. S c i . 1984, 24, 33. 20. Tomellini, S.A.; Stevenson, J.M.; Woodruff, H.B. Anal. Chem. 1984, 56, 67 21. Tomellini, S.A.; Hartwick,R.A.;Stevenson, J.A.; Woodruff, H.B. Anal. Chim. Acta 1984, 162, 227. 22. Tomellini, S.A.; Hartwick, R.A.; Woodruff, H.B. Appl. Spectrosc. 1985, 39, 331. 23. Saperstein, D.D.; "A Scheme For Optimized Infrared Interpretations", paper # 216, 1985. Pittsburgh Conference & Exposition on A n a l y t i c a l Chemistry and Applied Spectroscopy, Feb. 25-March 1, 1985. 24. DeHaseth, J.A.; Mir, K.A., "A Minicomputer Based Structure E l u c i dation Program", paper # 217, 1985. Pittsburgh Conference & Exposition on A n a l y t i c a l Chemistry and Applied Spectroscopy, Feb. 25-March 1, 1985.

RECEIVED December 17, 1985


Artificial Intelligence Applications in Chemistry - American Chemical

Recommend Documents